{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Exploration (exploratory data analysis or EDA)\n",
    "\n",
    "The goal here is to find out more about the data and become a subject matter export on the dataset you're working with. \n",
    "\n",
    "1. What question(s) are you trying to solve?\n",
    "2. What kind of data do we have and how do we treat different types?\n",
    "3. What's missing from the data and how do you deal with it?\n",
    "4. Where are the outliers and why should you care about them?\n",
    "5. How can you add, change or remove features to get more out of your data?`\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd \n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv(\"data.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1216, 11)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    .dataframe thead th {\n",
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       "      <th>score</th>\n",
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       "      <th>num_of_comments</th>\n",
       "      <th>body</th>\n",
       "      <th>author</th>\n",
       "      <th>comments</th>\n",
       "      <th>flair</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>combined_features</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>gelatine available</td>\n",
       "      <td>jk9zlt</td>\n",
       "      <td>0</td>\n",
       "      <td>https://www.reddit.com/r/india/comments/jk9zlt...</td>\n",
       "      <td>4</td>\n",
       "      <td>wish buy gelatine looking cheapest local price...</td>\n",
       "      <td>csstudentG</td>\n",
       "      <td>available regular kirana stores usually kept r...</td>\n",
       "      <td>AskIndia</td>\n",
       "      <td>2020-10-29 21:26:38</td>\n",
       "      <td>gelatine availableavailable regular kirana sto...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>trevor noah jokes indo pak tensions seriousnes...</td>\n",
       "      <td>gv9lmh</td>\n",
       "      <td>37</td>\n",
       "      <td>https://www.reddit.com/r/india/comments/gv9lmh...</td>\n",
       "      <td>29</td>\n",
       "      <td>really watch much trevor noah late night show ...</td>\n",
       "      <td>CommYouNitty</td>\n",
       "      <td>mostly country really matter lives pretty chea...</td>\n",
       "      <td>AskIndia</td>\n",
       "      <td>2020-06-02 23:42:24</td>\n",
       "      <td>trevor noah jokes indo pak tensions seriousnes...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>buying used bs4 scooty april 1 issues registra...</td>\n",
       "      <td>hpqq5o</td>\n",
       "      <td>11</td>\n",
       "      <td>https://www.reddit.com/r/india/comments/hpqq5o...</td>\n",
       "      <td>7</td>\n",
       "      <td>know right place ask askindia asking saw artic...</td>\n",
       "      <td>akza07</td>\n",
       "      <td>scammers keep scamming applies new vehicles bu...</td>\n",
       "      <td>AskIndia</td>\n",
       "      <td>2020-07-12 15:58:15</td>\n",
       "      <td>buying used bs4 scooty april 1 issues registra...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>need feedback insurance policy took x post ask...</td>\n",
       "      <td>1s57oi</td>\n",
       "      <td>1</td>\n",
       "      <td>https://www.reddit.com/r/india/comments/1s57oi...</td>\n",
       "      <td>1</td>\n",
       "      <td>posting lack activity r askindia hello last ye...</td>\n",
       "      <td>dhavalcoholic</td>\n",
       "      <td>dear policy holder dhavalcoholic request help ...</td>\n",
       "      <td>AskIndia</td>\n",
       "      <td>2013-12-05 14:30:23</td>\n",
       "      <td>need feedback insurance policy took x post ask...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>could suggest good dramedies</td>\n",
       "      <td>h7t0r2</td>\n",
       "      <td>5</td>\n",
       "      <td>https://www.reddit.com/r/india/comments/h7t0r2...</td>\n",
       "      <td>4</td>\n",
       "      <td>r askindia restricted posting sure ask hope gu...</td>\n",
       "      <td>dont_mess_with_tx</td>\n",
       "      <td>vicky donor jolly llb angoor comedy guide dram...</td>\n",
       "      <td>AskIndia</td>\n",
       "      <td>2020-06-13 04:58:52</td>\n",
       "      <td>could suggest good dramediesvicky donor jolly ...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                               title      id  score  \\\n",
       "0                                 gelatine available  jk9zlt      0   \n",
       "1  trevor noah jokes indo pak tensions seriousnes...  gv9lmh     37   \n",
       "2  buying used bs4 scooty april 1 issues registra...  hpqq5o     11   \n",
       "3  need feedback insurance policy took x post ask...  1s57oi      1   \n",
       "4                       could suggest good dramedies  h7t0r2      5   \n",
       "\n",
       "                                                 url  num_of_comments  \\\n",
       "0  https://www.reddit.com/r/india/comments/jk9zlt...                4   \n",
       "1  https://www.reddit.com/r/india/comments/gv9lmh...               29   \n",
       "2  https://www.reddit.com/r/india/comments/hpqq5o...                7   \n",
       "3  https://www.reddit.com/r/india/comments/1s57oi...                1   \n",
       "4  https://www.reddit.com/r/india/comments/h7t0r2...                4   \n",
       "\n",
       "                                                body             author  \\\n",
       "0  wish buy gelatine looking cheapest local price...         csstudentG   \n",
       "1  really watch much trevor noah late night show ...       CommYouNitty   \n",
       "2  know right place ask askindia asking saw artic...             akza07   \n",
       "3  posting lack activity r askindia hello last ye...      dhavalcoholic   \n",
       "4  r askindia restricted posting sure ask hope gu...  dont_mess_with_tx   \n",
       "\n",
       "                                            comments     flair  \\\n",
       "0  available regular kirana stores usually kept r...  AskIndia   \n",
       "1  mostly country really matter lives pretty chea...  AskIndia   \n",
       "2  scammers keep scamming applies new vehicles bu...  AskIndia   \n",
       "3  dear policy holder dhavalcoholic request help ...  AskIndia   \n",
       "4  vicky donor jolly llb angoor comedy guide dram...  AskIndia   \n",
       "\n",
       "             timestamp                                  combined_features  \n",
       "0  2020-10-29 21:26:38  gelatine availableavailable regular kirana sto...  \n",
       "1  2020-06-02 23:42:24  trevor noah jokes indo pak tensions seriousnes...  \n",
       "2  2020-07-12 15:58:15  buying used bs4 scooty april 1 issues registra...  \n",
       "3  2013-12-05 14:30:23  need feedback insurance policy took x post ask...  \n",
       "4  2020-06-13 04:58:52  could suggest good dramediesvicky donor jolly ...  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>id</th>\n",
       "      <th>score</th>\n",
       "      <th>url</th>\n",
       "      <th>num_of_comments</th>\n",
       "      <th>body</th>\n",
       "      <th>author</th>\n",
       "      <th>comments</th>\n",
       "      <th>flair</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>combined_features</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1211</th>\n",
       "      <td>fact check bjp claim bihar defeated coronaviru...</td>\n",
       "      <td>j002jl</td>\n",
       "      <td>321</td>\n",
       "      <td>https://thewire.in/politics/bjp-bihar-election...</td>\n",
       "      <td>43</td>\n",
       "      <td>NaN</td>\n",
       "      <td>mubukugrappa</td>\n",
       "      <td>2021 assam set example control covid model rep...</td>\n",
       "      <td>Coronavirus</td>\n",
       "      <td>2020-09-26 13:23:26</td>\n",
       "      <td>fact check bjp claim bihar defeated coronaviru...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1212</th>\n",
       "      <td>india us travel</td>\n",
       "      <td>j0wucw</td>\n",
       "      <td>0</td>\n",
       "      <td>https://www.reddit.com/r/india/comments/j0wucw...</td>\n",
       "      <td>0</td>\n",
       "      <td>anyone travelled fro united states india recen...</td>\n",
       "      <td>pizzapuff93</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Coronavirus</td>\n",
       "      <td>2020-09-28 03:08:39</td>\n",
       "      <td>india us travelnanhttps://www.reddit.com/r/ind...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1213</th>\n",
       "      <td>parents tested results</td>\n",
       "      <td>izx7aw</td>\n",
       "      <td>4</td>\n",
       "      <td>https://www.reddit.com/r/india/comments/izx7aw...</td>\n",
       "      <td>1</td>\n",
       "      <td>father pa became coronavirus positive dad mom ...</td>\n",
       "      <td>spddgr8</td>\n",
       "      <td>tn apparently bother contacting negative</td>\n",
       "      <td>Coronavirus</td>\n",
       "      <td>2020-09-26 10:12:50</td>\n",
       "      <td>parents tested resultstn apparently bother con...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1214</th>\n",
       "      <td>hey fellow indians wanted remind half year day...</td>\n",
       "      <td>ix6pl0</td>\n",
       "      <td>1019</td>\n",
       "      <td>https://www.reddit.com/gallery/ix6pl0</td>\n",
       "      <td>153</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IndianPuppy</td>\n",
       "      <td>saying curfew ended people indore went outdoor...</td>\n",
       "      <td>Coronavirus</td>\n",
       "      <td>2020-09-22 02:55:01</td>\n",
       "      <td>hey fellow indians wanted remind half year day...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1215</th>\n",
       "      <td>india secrecy narendra modi coronavirus relief...</td>\n",
       "      <td>iwafpe</td>\n",
       "      <td>293</td>\n",
       "      <td>https://theconversation.com/india-why-secrecy-...</td>\n",
       "      <td>9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9kSs</td>\n",
       "      <td>secrecy narendra modi coronavirus relief fund ...</td>\n",
       "      <td>Coronavirus</td>\n",
       "      <td>2020-09-20 16:56:43</td>\n",
       "      <td>india secrecy narendra modi coronavirus relief...</td>\n",
       "    </tr>\n",
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       "                                                  title      id  score  \\\n",
       "1211  fact check bjp claim bihar defeated coronaviru...  j002jl    321   \n",
       "1212                                    india us travel  j0wucw      0   \n",
       "1213                             parents tested results  izx7aw      4   \n",
       "1214  hey fellow indians wanted remind half year day...  ix6pl0   1019   \n",
       "1215  india secrecy narendra modi coronavirus relief...  iwafpe    293   \n",
       "\n",
       "                                                    url  num_of_comments  \\\n",
       "1211  https://thewire.in/politics/bjp-bihar-election...               43   \n",
       "1212  https://www.reddit.com/r/india/comments/j0wucw...                0   \n",
       "1213  https://www.reddit.com/r/india/comments/izx7aw...                1   \n",
       "1214              https://www.reddit.com/gallery/ix6pl0              153   \n",
       "1215  https://theconversation.com/india-why-secrecy-...                9   \n",
       "\n",
       "                                                   body        author  \\\n",
       "1211                                                NaN  mubukugrappa   \n",
       "1212  anyone travelled fro united states india recen...   pizzapuff93   \n",
       "1213  father pa became coronavirus positive dad mom ...       spddgr8   \n",
       "1214                                                NaN   IndianPuppy   \n",
       "1215                                                NaN          9kSs   \n",
       "\n",
       "                                               comments        flair  \\\n",
       "1211  2021 assam set example control covid model rep...  Coronavirus   \n",
       "1212                                                NaN  Coronavirus   \n",
       "1213           tn apparently bother contacting negative  Coronavirus   \n",
       "1214  saying curfew ended people indore went outdoor...  Coronavirus   \n",
       "1215  secrecy narendra modi coronavirus relief fund ...  Coronavirus   \n",
       "\n",
       "                timestamp                                  combined_features  \n",
       "1211  2020-09-26 13:23:26  fact check bjp claim bihar defeated coronaviru...  \n",
       "1212  2020-09-28 03:08:39  india us travelnanhttps://www.reddit.com/r/ind...  \n",
       "1213  2020-09-26 10:12:50  parents tested resultstn apparently bother con...  \n",
       "1214  2020-09-22 02:55:01  hey fellow indians wanted remind half year day...  \n",
       "1215  2020-09-20 16:56:43  india secrecy narendra modi coronavirus relief...  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Diving deep into the data to know more about it"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Policy/Economy        100\n",
       "Photography           100\n",
       "AskIndia              100\n",
       "Coronavirus           100\n",
       "Non-Political         100\n",
       "Business/Finance      100\n",
       "Science/Technology    100\n",
       "Sports                100\n",
       "AMA                   100\n",
       "Food                  100\n",
       "Scheduled             100\n",
       "Politics              100\n",
       "[R]eddiquette          16\n",
       "Name: flair, dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['flair'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data['flair'].value_counts().plot(kind = 'bar');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1216 entries, 0 to 1215\n",
      "Data columns (total 11 columns):\n",
      " #   Column             Non-Null Count  Dtype \n",
      "---  ------             --------------  ----- \n",
      " 0   title              1216 non-null   object\n",
      " 1   id                 1216 non-null   object\n",
      " 2   score              1216 non-null   int64 \n",
      " 3   url                1216 non-null   object\n",
      " 4   num_of_comments    1216 non-null   int64 \n",
      " 5   body               733 non-null    object\n",
      " 6   author             1216 non-null   object\n",
      " 7   comments           1103 non-null   object\n",
      " 8   flair              1216 non-null   object\n",
      " 9   timestamp          1216 non-null   object\n",
      " 10  combined_features  1216 non-null   object\n",
      "dtypes: int64(2), object(9)\n",
      "memory usage: 104.6+ KB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "title                  0\n",
       "id                     0\n",
       "score                  0\n",
       "url                    0\n",
       "num_of_comments        0\n",
       "body                 483\n",
       "author                 0\n",
       "comments             113\n",
       "flair                  0\n",
       "timestamp              0\n",
       "combined_features      0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = data.columns\n",
    "x = data.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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SQwavJEkNGbySJDVk8EqS1JDBK0lSQwavJEkNGbySJDVk8EqS1JDBK0lSQwavJEkNGbySJDVk8EqS1JDBK0lSQwavJEkNGbySJDVk8EqS1JDBK0lSQwavJEkNGbySJDVk8EqS1JDBK0lSQwavJEkNGbySJDVk8EqS1JDBK0lSQwavJEkNGbySJDVk8EqS1JDBK0lSQwavJEkNGbySJDXUPHiTHJzkqiRXJzmu9ftLkjSkpsGbZHvgn4DHA/sCz0iyb8saJEkaUus93gOAq6vqy1X1Y+AM4NDGNUiSNJhUVbs3S54OHFxVz+2Xnwk8vKr+eNp6RwNH94v3B65qVuTs7gl8fegiJoytHhhfTdYzv7HVZD3zG1tNY6sHxlPTfapq2fTGHRoXkRnaNkn+qjoFOGXxy9l8SdZV1aqh65gytnpgfDVZz/zGVpP1zG9sNY2tHhhnTZNaH2peD+w1sbwcuL5xDZIkDaZ18P4HsDLJ3kl+DlgNnNO4BkmSBtP0UHNV3Zrkj4F/B7YHTq2qy1vWsACjOvTN+OqB8dVkPfMbW03WM7+x1TS2emCcNd2uaecqSZLu7By5SpKkhgxeSZIaMnglSWrI4JUkqaHWA2gsOUnuVlXfH7iGp871fFW9s1Ut0yXZGfilqhrD6GLaDEl2r6pvDl0H3D5++79X1W8NXcsY9T+fk6rqT4euZbokuwBVVTcPXctS4x7vLJI8MskVwJX98oOSnDxQOU/ub0cBbwZ+r7+9CThioJpI8mTgIuCD/fKDkwxyXXaSl8x1G6KmidqOTbJLOm9OcmGSxw5Y0ueSvCPJE5LMNJpcM1V1G/CDJLsOWcekMW2v/ufzsKG306Qkq5JcClwCXJbk4iQPG7imV/bb7C5J1ib5epLBPhvnY/DO7u+BxwHfAKiqi4FfH6KQqnp2VT2bbnjNfavqaVX1NGC/IeqZ8Bd0E198G6CqLgJWDFTL3ee5Dek5VfVd4LHAMuDZwEkD1nM/uuscnwlcneSvk9xvwHp+BFzah9xrp24D1jO27fV54Owkz0zy1KnbgPWcCjy/qlZU1X2AY4DTBqwH4LH9NnsS3QiJ9wNGd5Rgioea51BV1037onnbULX0VlTVDRPLN9L9gg3l1qr6zhi+jFfVX/aH5V5YVX8/dD3TTP2AngCcVlUXD7kHU93F++cC5yZ5NPAvwPOTXAwcV1WfaVzS+/rbWIxqewG70+0APGairYChTjHdXFWfuL2Qqk8mGfpw8136+ycAp1fVN8fwuTQbg3d21yV5JFD98JYvpD/sPKCPJvl34HS6P7zVwHkD1nNZkt8Ftk+yku5n9Omhiqmq25IcQne0YkwuSPIhYG/g+CR3B346VDFJfoHuFMUz6b68vYBu6NYHA+/o62ymqtb0f2NTXyKvqqqftKxhmlFtr/5o15icn+SN3PE59Dt0n00PBaiqCweo6T1JvgD8kO5L5DK6Iymj5MhVs0hyT+Afgd+i+wb8IeDYqvrGwHU9Ffgf/eLHq+pdA9ZyV+DP6Q7JQTcU6F9V1WC/8ElOBHYF/hW4vVPcQB8GUzVtRxdqX66qb/fBt2dVXTJQPV8E/plub279tOdeVlV/27ie3wTWAF+h+1vbCziyqj7eso6JekaxvZK8tKpemeR1zDyL2wtb1jMlyVxf9quqHjPH84smyW7Ad/sv4HcD7l5VXxuilvkYvNoiY+2NOvGhMPWLHQb8MABIsraqDpqvrWE9qRH94Se5APjdqZ7x/fnm06tqkA47Y9leSZ5cVe9JcuRMz1fVmpb1jFm/E/ASuissju6PwN2/qt47cGkz8lDzNLN9u5wyxLfMJJ+sqgP78yiTtU2Fyi6ta+q/Vf4gya5V9Z3W7z+Hj87QNkjIJNkJuCtwz/7b+NRJp12AXxygnvfQ/yxmOv9VVYe0rql3l8nL0arqi0nuMtcLFsPYtldVvae/H1XAJrkH8Cy6jpS3Z8hQe+C904ALgEf2y+vpTpsYvEvEuqELmK6qDuzvh+6dO91Ub9Rz2fiw7pB/gN+beLwTXS/Hoc7N/yHwIroP7Qu444P8u8A/DVDPq/r7pwL/ja5TFcAz6A7zDmVdkjfTHf6G7lK5CwaoY2zbC4D+fOXLgH3pfqcBGPAozvuBzwKXMuC572nuW1W/k+QZAFX1wzFdgjWdh5pnkeSwqnrHfG13ZkvhEFiSHYFzqupxA9bwgqp63VDvP12Sj1fVr8/X1rCeHekuSTmQLuw+DpxcVbcMVM/YtteH6Pos/AnwR8CRwIaqetlA9VxYVQ8d4r1nk+TTwEHAp6rqoUnuS3e64oCBS5uRwTuLmX65xvgLN7SR9UbdRH/I8PyqWjlwHY9k00Nzbx2oliuBJ1bVl/vlvYH3V9WvDFHPGI1se11QVQ9LcklV7d+3fayqfmOgel5Md2TpvcDtX46GHA0tyW8D/5vuqMCHgEcBv19VHx2qprl4qHmaJI+nuxZsz2kX8e8C3DpMVeM0U2/UJIP1Ru1rupQ7zuluTzcAwsuHqgcgyT8D96Ub5WvqWvACBvkgB15Md/nHl/vlFcDRA9VCkkfRDcZyHzYOun0Gqmds22vqy+wNSZ4IXA8sH6gWgB8Df0d3RcPU31oBQ22v7YDd6E6hPILus+jYqvr6EPVsDvd4p0nyILpLCV4O/N+Jp24Gzquqbw1S2AiNrTdqX8N9JhZvBW6sqkG/MPV7mPuOrCfxjsAD+sUvDHVYt6/lC3RfBi5gYpCaoS7dG9v2SvIk4BN0l1m9jm4n4C+raqjhWf8TePiYgm3IUyVbwj3eaaobGvLiJG8f22HTERpFb9RJVfVfQ77/LC6j68x0w3wrttBvoz/kjiFQP5rkjQP+vn+nqj4w0HvPZFTba+KSmO8Ajx6ylt7lwA+GLmKac5P8CZtevz+KyUCmc493miRnVtXh0w5Z3m7qHIsgyal0P6PJ3qg7jHCknUH11xY/GDifjc+JDXL5TpI30Q2xN9UJ7pnAbVX13MZ1TPWXOJzutMA72fjnM8igJ2PZXplnvOoBB9B4F9048eex8c9nsKsZklwzQ3MNdbpiPgbvNEn2qKobkpzJxoNsB3hlVR0+UGmjM7beqGOVZMZOMFX1sda1ACS5uKoeNF9bgzrGOgLSKLZXkvV051F3AzY5xTXU1QNL4WqGsTN4ZzFLr+ZL3OO9Qz8s24+qm7psajSrHatqbIehBtefe15ZVR/uR9nZvgaaxzTJhcBhVfWf/fI+wFlD9dhPss9UD+u52hrXNPj2Sjct6ePpxtHe5BDzWA+jDiHJs2ZqH6on+nw8xztNkucBzwf2STI5NuvdgU8NU9VoraUby3pq0Iqd6bryP3LWV9wJJfkDul7Du9P1lt0TeAPddYdD+FPgvGm9moc8PXAWMD303wEMNWTkWLbXG+jmut6HjQf2CcP2Il4J/A2bDugx5GHdX514vBPdtrqQ4Xqiz8ng3dTbgQ/Q/WIdN9F+s98wN7FTVd0+UlRVfa/fO9DGjqGbt/hzAFX1pST3GrCeTwFv5I4geSPQeipAkjyA7lzhrtl4ftldmPhAH8AotldVvRZ4bZLXV9XzWr//HE4DTqCbBezRdF/aBh0lqqpeMLmcZFfu6HsyOgbvNP24w9+hG0ZPc/t+kodOdYJJsopuWi5t7Jaq+vHUCHZJdmCg8aN7b6UbBvEV/fIz6D6kDmtcx/3phvS8B/DkifabgT9oXMukUW2vkYUuwM5VtTZJ+qsI/iLJJ+jCeCx+AAw6aM5cDF4txLHAO5JcT/fB9It0c3NqYx9L8mfAzv0IO88H3jNgPfef1pHqvCQXty6iqs4Gzk7ya1XVfI97DmPbXmPzo37Qii8l+WPgq8CQR3A2mgAE2I7uMPhoh/e1c5W2WJLD6Obg/SXgf9KNGvN/hroMZKz6D6mj6OYtDt3P7E1DDdCQ5C3AG6rqs/3yw+nmv33+QPWcxsyX7j1ngHJGt73GJsmv0k08cg+6oya70F3x8bkBa5rsiX4r8F81ba7pMTF4tcWmenknORD4a+DVwJ9V1cMHLk0zmLg2/S50h3mv7ZfvA1xRVQ8cqK6nTSzuRPcl7vohrwvV7DLCCWSS/O30SSNmahsLg1dbLMnnq+ohSf4GuLSq3j7VNnRtY9IP+fcK7hiLeJB5lKcNp7mJsYz61e9xfnjA63hHsb3GapZLLQedQGapXf7pOV4txFeTvJHukqK/7QfU2G7gmsboH+gGcL90yMOVYwnWzbCS7vTFUEaxvcZmjBPILNXLPw1eLcThwMHAq6rq20n2YOPRvtS5DrjMD/GZJbmZjWe5uRF46XAVub1mcT3d9cSH0E1oMeVmukkuhrAkL//0ULO0yPrOKK8APsbGY9u+ZrCiRibJ7nR7ulPX71YNNL2k22tuSe4yNaFGuvmu96qqS+Z5WRP99daTg3pcO2A5s3KPV1p8J9KN7rUT8HMD1zI6SZ5Ld2nacro5cB9BN6DHIOd4cXvN59wkh9Dlx0XAhiQfq6qXDFVQkicDr6G7pPEmuvPzV9IN0DI6Bq+0+HavqscOXcSIHUs35N9nq+rR/YhWfzlgPW6vue1aVd/tvzCdVlUnTDu/OoS/ovvC9uG+w+ejGfEgSHaEkRbfh5P4QT67H1XVj6Cb8aqqvkB3udNQ3F5z26Hvz3E48N75Vm7kJ1X1DWC7JNtV1dTUjqPkHq+0+I4BXprkFuAneHnKdOuT3AN4N91hzG/RdeQZittrbi+nG1TkU1X1H/3sVl8auKZvJ/l54BPA25LcxEA9rTeHnaskjUY/AtGuwAer6sdD16OloZ+i9Id0R3F/j+536G39XvDoGLxSA0n2p5t+7/ajTFX1zsEK0pzcXrNLcj/g9cC9q+qB/c/qkKr6q4HrGnwO5c1l8EqLLMmpwP7A5cBP++Yaaixizc3tNbckH6O7Xv+NU6PUJblsqCFH+/e/fQ7lqrpvP2fwG6pqqDmv5+Q5XmnxPaKq9h26CG02t9fc7lpV509Nm9gb+nzqKOZQ3lz2apYW32eS+EG+dLi95vb1JPelH20sydOBG4YtqZtDeWph6DmU5+Mer7T41tB9mH+NbiSkqV6yoxzAXW6veRwDnAI8IMlXgWuAI4YtaWnNoew5XmmRJbkaeAlwKXecM1xKkxbcqbi9Nk/fk3i7MXRgWmpzKBu80iJL8pGhprjTz87tNbf+mutnsWmv7+bzJydZW1UHjXnu3Zl4qFlafF9I8na6Q1+Tg+57eco4ub3m9n7gs0w7IjCQPfprvw9Jcgbd3u7tqurCYcqam3u80iJLctoMzV6eMlJur7kNPen9pL5j11HAgXRTFk6qsR65MHglSZstyYvpZm96LxsfERhs/tsk/6eqXjHH8/tV1eUta5qLlxNJiyzJ8iTvSnJTkhuT/FuS5UPXpZm5veb1Y+Dv6KZuvKC/Td/bbGqu0O39c5NCNpPBKy2+04Bz6OYK3ZPu3OFMhzM1Dm6vub0E+OWqWlFVe/e3fYYuah6Zf5V2DF5p8S2rqtOq6tb+9hZg2dBFaVZur7ldDvxg6CJ+RqM6p2qvZmnxfT3JEcDp/fIzgFHOmiLA7TWf24CLkpzHxud4m19OtFQZvNLiew7w/4C/p/vm/em+TeM00/Z69qAVjcu7+9tSMqopJu3VLEkTkqwBXlRV3+qXdwde5eVE45Nkzsuaxnodr3u80iLrP8iPrapv98u7Aa/2g3y09p8KXeguk0nykCELGoMkZ1bV4UkuZdNzplVVDxqgrFf39zsBq4CL6TpS7U83U9GBA9Q0L4NXWnz7T4UuQFV9yw/yUdsuyW7T9nj9rIRj+/sr6ebjnRLgle3Lgap6NEA/atXRVXVpv/xA4E+GqGlz+MskLT4/yJeWVwOfTnIW3Z7d4cCJw5Y0vKqamvrvl6dPGJHkAQOUNOkBU6ELUFWXJXnwkAXNxT9+afH5Qb6EVNVbk6wDHkO3N/fUqrpi4LIGl+R5dNPt7ZPkkomn7g58apiqbndlkjcB/0L3N3YE3Z75KNm5Smqgn1h96oN87eQH+eTesDRWSXYFdgP+Bjhu4qmbhxwuEiDJTsDzgF/vmz4OvL6qfjRcVbMzeKWBjWnQeWmpSrIz8EtVddXQtczHkauk4Y1qODtpqUlyCNNFhR8AAASfSURBVHAR8MF++cFJzhm2qtkZvNLwPOwkLcwJwAHAtwGq6iJgxZAFzcXglSQtdbdW1XeGLmJzGbzSIkmy9+auuqiFSNu+y5L8LrB9kpVJXkc31OcoGbzS4jkLIMnaedY7qEEt0rbsBcB+dJM2nA58F3jRoBXNwV7N0iJJ8nm6weSfSzfg/kaq6jXNi5I0OAfQkBbPauApdH9ndx+4FmmbleR+dENErmAi16rqMUPVNBf3eKVFluTxVfWBoeuQtlVJLgbeAFxAN18wAFV1wWBFzcHglRZZP+LPCdwxqs7HgJcvpV6Y0pgluaCqHjZ0HZvLzlXS4jsVuJlujObD6Tp+nDZoRdK25T1Jnp9kjyS7T92GLmo27vFKiyzJRVX14PnaJG2ZJNfM0FxVtU/zYjaDnaukxffDJAdW1ScBkjwK+OHANUnbjKra3GvmR8E9XmmRJXkQ8FZg177pW8CRVXXJ7K+SNJ8kj6mqjyR56kzPV9U7W9e0OdzjlRZZVV0MPCjJLv3ydyefT3JkVa0ZpDhpafsN4CPAk2d4roBRBq97vNLAnBZQunOxV7M0PMdqlhYgyS8keW2SC5NckOQfk/zC0HXNxuCVhudhJ2lhzgA2AE8Dnt4//tdBK5qDh5qlgSX5fFU9ZOg6pKVqpgE0kqyrqlVD1TQX93il4X1q6AKkJe68JKuTbNffDgfeN3RRs3GPV1pkSe4BPItNB3B/4VA1SduCJDfTnaoJcDfgp/1T2wHfq6pdhqptLl5OJC2+9wOfBS7ljg8GSQtUVUty1i/3eKVF5uVC0uJLsj+bHlXyOl7pzijJi4HvAe8Fbplqr6pvDlaUtA1JciqwP3A5dxxVqqp6znBVzc7glRZZkmOAE4Fvc8elQ6MdwF1aapJcUVX7Dl3H5vIcr7T4XgL8clV9fehCpG3UZ5LsW1VXDF3I5jB4pcV3OfCDoYuQtmFr6ML3a3Snc0J3VGn/YcuamcErLb7bgIuSnMfG53i9nEjaOk4FnskSuXLA4JUW37v7m6TFcW1VnTN0EZvLzlWSpCUtycnAPYD3sPFRpVFeTuQer7TIklzDDBMh2KtZ2mp2pgvcx060jXY+XoNXWnyTA7XvBBwG7D5QLdI2p6qePXQNPwsPNUsDSPLJqjpw6DqkbUGS5cDrgEfR7el+Eji2qtYPWtgs3OOVFlmSyeEit6PbA16SY8xKI3Ua8Ha6o0kAR/Rtvz1YRXNwj1daZP1lRFN/aLcCXwFeVVVfHKwoaRuS5KKqevB8bWPhfLzS4ns88GZgLd3cu18FVg9akbRt+XqSI5Js39+OAL4xdFGzMXilxfdu4MnAT+gmS/ge8P1BK5K2Lc8BDge+BtwAPB0YbYcrDzVLiyzJZVX1wKHrkLZVSdYAL6qqb/XLu9Odzhnl7ETu8UqL79NJ/vvQRUjbsP2nQhdun3LzIQPWMyd7NUuL70Dg9/uBNEY/gLu0BG2XZLdpe7yjzbfRFiZtQx4/dAHSNu7VdEeWzqK7guBwujmwR8lzvJKkJS/JvsBj6I4orR3z3LwGryRJDdm5SpKkhgxeSZIaMnglSWrI4JUkqaH/D8p2LZ+tOcGPAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "ax = fig.add_axes([0,0,1,1])\n",
    "ax.bar(y,x)\n",
    "plt.xticks(rotation=90)\n",
    "plt.savefig('value.png', dpi=300, bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>score</th>\n",
       "      <th>num_of_comments</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1216.000000</td>\n",
       "      <td>1216.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>229.009046</td>\n",
       "      <td>50.039474</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>919.346846</td>\n",
       "      <td>181.467094</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>8.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>28.500000</td>\n",
       "      <td>11.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>122.500000</td>\n",
       "      <td>39.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>18853.000000</td>\n",
       "      <td>4166.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              score  num_of_comments\n",
       "count   1216.000000      1216.000000\n",
       "mean     229.009046        50.039474\n",
       "std      919.346846       181.467094\n",
       "min        0.000000         0.000000\n",
       "25%        8.000000         3.000000\n",
       "50%       28.500000        11.000000\n",
       "75%      122.500000        39.250000\n",
       "max    18853.000000      4166.000000"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>score</th>\n",
       "      <th>num_of_comments</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>score</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.639001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>num_of_comments</th>\n",
       "      <td>0.639001</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    score  num_of_comments\n",
       "score            1.000000         0.639001\n",
       "num_of_comments  0.639001         1.000000"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['title', 'id', 'score', 'url', 'num_of_comments', 'body', 'author',\n",
       "       'comments', 'flair', 'timestamp', 'combined_features'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualising the data on the basis of data entry"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7fe41aeacdf0>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "ax.scatter(data['timestamp'][:1000], data['flair'][:1000])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('O')"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['timestamp'].dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv('data.csv',parse_dates=['timestamp'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('<M8[ns]')"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['timestamp'].dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "ax.scatter(data['timestamp'][:1000], data['flair'][:1000]);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualising the span of few properties "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0                                   gelatine available\n",
       "1    trevor noah jokes indo pak tensions seriousnes...\n",
       "2    buying used bs4 scooty april 1 issues registra...\n",
       "3    need feedback insurance policy took x post ask...\n",
       "4                         could suggest good dramedies\n",
       "5    reality check exposure western society media b...\n",
       "6                      askindia cingari cengar tzengar\n",
       "7                               truly incredible india\n",
       "8    people left 9 5 jobs pursue career music art f...\n",
       "9    4 days ago pending orders 100 million hydroxyc...\n",
       "Name: title, dtype: object"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['title'][:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "temp = data['title'].str.len()\n",
    "temp.hist(bins = np.arange(0,200,1))\n",
    "plt.title(\"no of words in TITLE\")\n",
    "plt.xlabel(\"length of Title (in words)\", fontsize=12)\n",
    "plt.ylabel(\"Number of posts\", fontsize=12)\n",
    "plt.savefig('title.png', dpi=300, bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "temp = data['body'].str.len()\n",
    "temp.hist(bins = np.arange(0,1000,10))\n",
    "plt.title(\"no of words in BODY\")\n",
    "plt.xlabel(\"length of BODY (in words)\")\n",
    "plt.ylabel(\"Number of posts\")\n",
    "plt.savefig('lenth of words in body.png', dpi=300, bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualising how the flairs fare according to other features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "temp = data.groupby(\"flair\").mean()[[\"num_of_comments\"]]\n",
    "\n",
    "temp.plot(kind='bar', legend=False, grid=True)\n",
    "plt.title(\"Average no of coments per flair\")\n",
    "\n",
    "plt.xlabel(\"Flair\")\n",
    "plt.ylabel(\"Average no of comments per post\")\n",
    "plt.savefig('comms per flair', dpi=300, bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "temp = data.groupby(\"flair\").mean()[['score']]\n",
    "\n",
    "temp.plot(kind='bar', legend=False, grid=True)\n",
    "plt.title(\"Average score per flair\")\n",
    "\n",
    "plt.xlabel(\"Flair\")\n",
    "plt.ylabel(\"Average score per post\")\n",
    "plt.savefig('score per flair', dpi=300, bbox_inches='tight')\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
